Machine vision measuring systems that have cameras are used in many applications. For example, wheels of motor vehicles may be aligned on an alignment rack using a computer-aided, three-dimensional (3D) machine vision alignment apparatus and a related alignment method. Targets are attached to the wheels of the vehicle to be aligned. Cameras of the alignment apparatus view the targets and form images of the targets. A computer in the apparatus analyzes the images of the targets to determine wheel position, and guides an operator in properly adjusting the wheels to accomplish precise alignment.
Examples of methods and apparatus useful in 3D alignment of motor vehicles are described in U.S. Pat. No. 5,943,783, Method and apparatus for determining the alignment of motor vehicle wheels, U.S. Pat. No. 5,809,658, Method and apparatus for calibrating cameras used in the alignment of motor vehicle wheels, U.S. Pat. No. 5,724,743, Method and apparatus for determining the alignment of motor vehicle wheels, and U.S. Pat. No. 5,535,522, Method and apparatus for determining the alignment of motor vehicle wheels. The apparatus described in these references is sometimes called a "3D aligner" or "aligner."
An example of a commercial embodiment of an aligner is the Visualiner 3D, commercially available from John Bean Company, Conway, Ark., a unit of Snap-on Tools Company.
To determine the alignment of the motor vehicle wheels, such 3D aligners use cameras that view targets affixed to the wheels. Each target comprises numerous marks that are used for the purpose of determining target position ("fiducials"). Proper operation of the aligner requires the aligner to create an image and recognize most of the fiducials on a target at any given time.
However, such aligners are normally installed in an automotive shop or other environment that is an inherently dirty environment. Normal handling of the targets by technicians can result in the targets becoming dirty. Grease, dirt or other contaminants may be deposited on the targets, obscuring one or more fiducials of the targets. Further, with some kinds of aligners that have "floating" booms and cameras, movement of the booms or cameras can cause placement of the cameras in a position at which the cameras can see only part of a target and therefore form only an incomplete image of a target.
In one current approach, if the aligner cannot recognize enough fiducials of a target, as a result of dirt, other contamination or obscuration of fiducials, or obstruction of the target, it cannot determine the location of that target, and stops operating. Although this approach ensures that the aligner operates based on an accurate view of the targets, a drawback is that the operator is not always certain why the aligner stops operating. In particular, the operator may have insufficient information film the aligner to determine why operation has stopped. The aligner simply ceases operating and the operator may therefore assume that the aligner is malfunctioning when, in fact, a dirty target is the source of the fault. When the fault involves obstruction of the target or mis-alignment of a floating boom or cameras, the operator may visually inspect the targets and yet may be unable to determine why the aligner will not operate, or may incorrectly assume that the aligner hardware or software is faulty.
Based on the foregoing, there is a clear need in this field for an apparatus and method that provides for automatic identification of faults in a machine vision measuring system.
There is a particular need for an aligner that can identify faults such as electronic noise, environmental contamination of targets, etc., and report information about the fault to an operator so that remedial action can be taken. There is also a need for an aligner that can suggest remedial action to be taken by an operator in response to detecting a fault.